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Evaluation of SMOS soil moisture products over the CanEx-SM10 area

Najib Djamai, Ramata Magagi, Kalifa Goïta, Mehdi Hosseini, Michael H. Cosh, Aaron Berg, Brenda Toth
Journal of hydrology 2015 v.520 pp. 254-267
Soil Moisture and Ocean Salinity satellite, algorithms, correlation, ecosystems, meteorological data, monitoring, prairies, rain, soil heterogeneity, soil water, vegetation cover
The Soil Moisture and Ocean Salinity (SMOS) Earth observation satellite was launched in November 2009 to provide global soil moisture and ocean salinity measurements based on L-band passive microwave measurements. Since its launch, different versions of SMOS soil moisture products processors have been developed. The purpose of this study is to evaluate the processor versions 309, 400, 501 and 551 by comparing them to (a) soil moisture measurements from the Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) and from networks of permanent and temporary stations, and (b) other existing satellite-based soil moisture products (AMSR-E/NSIDC, AMSR-E/VUA, and ASCAT). Rainfall data were used during the analysis in order to understand the episodic variability of soil moisture. The analysis included both agricultural site (Canadian Prairies) and forested site (Boreal Ecosystem Research and Monitoring Sites; BERMS), and considered separately the SMOS ascending and descending modes. An improvement in SMOS soil moisture estimation was observed from the processor versions 309 to 551. We observed a little difference between the processor versions 400, 501, and particularly between the processor versions 501 and 551. These later versions were more correlated to ground measurements than the previous processor versions. For the agricultural site, all the four SMOS processor versions underestimated the soil moisture, but to varying degrees depending on the overpasses mode. For the ascending overpass, the four processor versions have a high bias with respect to the measured ground data (from −0.10m3/m3 to −0.12m3/m3). For the descending overpass, however, a good improvement in the algorithms was observed. Thus the maximum bias for the measured ground data went from −0.12m3/m3 for processor version 309 to −0.02m3/m3 for processor version 551, and the soil moisture error seems to be less dependent on the absolute soil moisture for the two last versions. Highest correlation coefficients with ground measurements were obtained with SMOS processor version 551 (R⩾0.58), ASCAT (R⩾0.55), and AMSR-E/NSIDC (R⩾0.54) products for ascending overpasses. For descending overpasses AMSR-E/NSIDC (R⩾0.82) is better correlated to ground measurements followed by SMOS (R⩾0.58) and ASCAT (R⩾0.32). However, AMSR-E/VUA appears weakly correlated with ground truth for both overpasses. Despite the good correlation found with ground data, the temporal evolution of AMSR-E/NSIDC data became stable with the vegetation growth and presented a weak sensitivity to rainfall. Over the forested site, SMOS soil moisture estimates were generally overestimated, especially before the active vegetation period where the bias obtained with prototype 551 was greater than 0.10m3/m3. Moreover, due to the denser and more complex vegetation cover, SMOS data were less correlated with the in situ data than for the Kenaston agricultural site. Soil moisture values from the ascending overpass were closer to the ground measurements (bias∼0.01m3/m3) than the estimates from the descending overpasses (0.09⩽bias⩽0.11m3/m3). ASCAT presented correlation coefficients to ground data comparable to those obtained by SMOS (version 551), whereas lower correlation coefficients were obtained with AMSR-E-NSIDC and mainly with AMSR-E/VUA data.